SoroushVahidi/scidocs huggingface dataset:
The differences in motivations of online game players and offline game players: A combined analysis of three studies at higher education level
(2011)

Computer games have become a highly popular form of entertainment and have had a large impact on how University students spend their leisure time. Due to their highly motivating properties computer games have come to the attention of educationalists who wish to exploit these highly desirable properties for educational purposes. Several studies have been performed looking at motivations for playing computer games in a general context and in a Higher Education (HE) context. These studies did not focus on the differences in motivations between online and offline game players. Equally the studies did not look at the differences in motivations of people who prefer single player games and people who prefer multiplayer games. If games-based learning is to become a recognised teaching approach then such motivations for playing computer games must be better understood. This paper presents the combined analysis of three studies at HE level, performed over a four year period from 2005 to 2009. The paper focuses on differences of motivations in relation to single player/multiplayer preference and online/ offline game participation. The study found that challenge is the top ranking motivation and recognition is the lowest ranking motivation for playing computer games in general. Challenge is also the top ranking motivation for playing games in HE while fantasy and recognition are the lowest ranking motivations for playing games in HE. Multiplayer gamers derive more competition, cooperation, recognition, fantasy and curiosity from playing games and online gamers derive more challenge, cooperation, recognition and control from playing games. Multiplayer gamers and online gamers ranked competition, cooperation and recognition significantly more important for playing games in HE than single players and offline participants. 2011 Elsevier Ltd. All rights reserved.
Homography-based 2D Visual Tracking and Servoing
(2007)

The objective of this paper is to propose a new homography-based approach to image-based visual tracking and servoing. The visual tracking algorithm proposed in the paper is based on a new efficient second-order minimization method. Theoretical analysis and comparative experiments with other tracking approaches show that the proposed method has a higher convergence rate than standard first-order minimization techniques. Therefore, it is well adapted to real-time robotic applications. The output of the visual tracking is a homography linking the current and the reference image of a planar target. Using the homography, a task function isomorphic to the camera pose has been designed. A new image-based control law is proposed which does not need any measure of the 3D structure of the observed target (e.g. the normal to the plane). The theoretical proof of the existence of the isomorphism between the task function and the camera pose and the theoretical proof of the stability of the control law are provided. The experimental results, obtained with a 6 d.o.f. robot, show the advantages of the proposed method with respect to the existing approaches. KEY WORDS—visual tracking, visual servoing, efficient second-order minimization, homography-based control law
Supervised tensor learning
(2005)

Tensor representation is helpful to reduce the small sample size problem in discriminative subspace selection. As pointed by this paper, this is mainly because the structure information of objects in computer vision research is a reasonable constraint to reduce the number of unknown parameters used to represent a learning model. Therefore, we apply this information to the vector-based learning and generalize the vector-based learning to the tensor-based learning as the supervised tensor learning (STL) framework, which accepts tensors as input. To obtain the solution of STL, the alternating projection optimization procedure is developed. The STL framework is a combination of the convex optimization and the operations in multilinear algebra. The tensor representation helps reduce the overfitting problem in vector-based learning. Based on STL and its alternating projection optimization procedure, we generalize support vector machines, minimax probability machine, Fisher discriminant analysis, and distance metric learning, to support tensor machines, tensor minimax probability machine, tensor Fisher discriminant analysis, and the multiple distance metrics learning, respectively. We also study the iterative procedure for feature extraction within STL. To examine the effectiveness of STL, we implement the tensor minimax probability machine for image classification. By comparing with minimax probability machine, the tensor version reduces the overfitting problem.
Two-Stream 3-D convNet Fusion for Action Recognition in Videos With Arbitrary Size and Length
(2018)

3-D convolutional neural networks (3-D-convNets) have been very recently proposed for action recognition in videos, and promising results are achieved. However, existing 3-D-convNets has two “artificial” requirements that may reduce the quality of video analysis: 1) It requires a fixed-sized (e.g., 112  $\times$ 112) input video; and 2) most of the 3-D-convNets require a fixed-length input (i.e., video shots with fixed number of frames). To tackle these issues, we propose an end-to-end pipeline named Two-stream 3-D-convNet Fusion, which can recognize human actions in videos of arbitrary size and length using multiple features. Specifically, we decompose a video into spatial and temporal shots. By taking a sequence of shots as input, each stream is implemented using a spatial temporal pyramid pooling (STPP) convNet with a long short-term memory (LSTM) or CNN-E model, softmax scores of which are combined by a late fusion. We devise the STPP convNet to extract equal-dimensional descriptions for each variable-size shot, and we adopt the LSTM/CNN-E model to learn a global description for the input video using these time-varying descriptions. With these advantages, our method should improve all 3-D CNN-based video analysis methods. We empirically evaluate our method for action recognition in videos and the experimental results show that our method outperforms the state-of-the-art methods (both 2-D and 3-D based) on three standard benchmark datasets (UCF101, HMDB51 and ACT datasets).
Fidelius Charm: Isolating Unsafe Rust Code
(2018)

The Rust programming language has a safe memory model that promises to eliminate critical memory bugs. While the language is strong in doing so, its memory guarantees are lost when any unsafe blocks are used. Unsafe code is often needed to call library functions written in an unsafe language inside a Rust program. We present Fidelius Charm (FC), a system that protects a programmer-specified subset of data in memory from unauthorized access through vulnerable unsafe libraries. FC does this by limiting access to the program's memory while executing unsafe libraries. FC uses standard features of Rust and utilizes the Linux kernel as a trusted base for splitting the address space into a trusted privileged region under the control of functions written in Rust and a region available to unsafe external libraries. This paper presents our design and implementation of FC, presents two case studies for using FC in Rust TLS libraries, and reports on experiments showing its performance overhead is low for typical uses.
Degree of Modularity in Engineering Systems and Products with Technical and Business Constraints
(2007)

There is consensus that modularity has many benefits from cost savings due to increased commonality to enabling a higher variety of products. Full modularity is, however, not always achievable. How engineering systems and products whose design is heavily influenced by technical constraints, such as weight or size limitations, tend to exhibit rather integral architectures is shown in this study. For this, two metrics are defined on the basis of a binary design structure matrix (DSM) representation of a system or product. The non-zero fraction (NZF) captures the sparsity of the interrelationships between components between zero and one, while the singular value modularity index (SMI) captures the degree of internal coupling, also between zero and one. These metrics are first developed using idealized canonical architectures and are then applied to two different product pairs that are functionally equivalent, but different in terms of technical constraints. Empirical evidence is presented that the lightweight variant of the same product tends to be more integral, presumably to achieve higher mass efficiency. These observations are strengthened by comparing the results to another, previously published, modularity metric as well as by comparing sparsity and modularity of a set of 15 products against a control population of randomly generated architectures of equivalent size and density. The results suggest that, indeed, some products are inherently less modular than others due to technological factors. The main advantage of SMI is that it enables analysis of the degree of modularity of any architecture independent of subjective module choices.
Applying the design structure matrix to system decomposition and integration problems: a review and new directions
(2001)

Systems engineering of products, processes, and organizations requires tools and techniques for system decomposition and integration. A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system that supports innovative solutions to decomposition and integration problems. The advantages of DSMsvis-à-visalternative system representation and analysis techniques have led to their increasing use in a variety of contexts, including product development, project planning, project management, systems engineering, and organization design. This paper reviews two types of DSMs, static and time-based DSMs, and four DSM applications: 1) Component-Basedor Architecture DSM, useful for modeling system component relationships and facilitating appropriate architectural decomposition strategies; 2) Team-Basedor Organization DSM, beneficial for designing integrated organization structures that account for team interactions; 3)Activity-Basedor Schedule DSM, advantageous for modeling the information flow among process activities; and 4)Parameter-Based(or low-level schedule)DSM, effective for integrating low-level design processes based on physical design parameter relationships. A discussion of each application is accompanied by an industrial example. The review leads to conclusions regarding the benefits of DSMs in practice and barriers to their use. The paper also discusses research directions and new DSM applications, both of which may be approached with a perspective on the four types of DSMs and their relationships.
Architectural Innovation : The Reconfiguration of Existing Product Technologies and the Failure of Established Firms
(2007)

Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms Author(s): Rebecca M. Henderson and Kim B. Clark Source: Administrative Science Quarterly, Vol. 35, No. 1, Special Issue: Technology, Organizations, and Innovation (Mar., 1990), pp. 9-30 Published by: Johnson Graduate School of Management, Cornell University Stable URL: http://www.jstor.org/stable/2393549 Accessed: 24/08/2008 13:09
Innovation : Mapping the winds of creative destruction *
(2003)

This paper develops a framework for analyzing the competitive implications of innovation. The framework is based on the concept of transilience the capacity of an innovation to influence the established systems of production and marketing. Application of the concept results in a categorization of innovation into four types. Examples from the technical history of the US auto industry are used to illustrate the concepts and their applicability. The analysis shows that the categories of innovation are closely linked to different patterns of evolution and to different managerial environments. Special emphasis is placed on the role of incremental technical change in shaping competition and on the possibilities for a technology based reversal in the process of industrial maturity.
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
(2002)

Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amount of labeled data required is to develop algorithms that can learn e ectively from a small number of labeled examples augmented with a large number of unlabeled examples. Current text learning techniques for combining labeled and unlabeled, such as EM and Co-Training, are mostly applicable for classi cation tasks with a small number of classes and do not scale up well for large multiclass problems. In this paper, we develop a framework to incorporate unlabeled data in the Error-Correcting Output Coding (ECOC) setup by rst decomposing multiclass problems into multiple binary problems and then using Co-Training to learn the individual binary classi cation problems. We show that our method is especially useful for text classi cation tasks involving a large number of categories and outperforms other semi-supervised learning techniques such as EM and Co-Training. In addition to being highly accurate, this method utilizes the hamming distance from ECOC to provide high-precision results. We also present results with algorithms other than co-training in this framework and show that co-training is uniquely suited to work well within ECOC.
Correcting the Triplet Selection Bias for Triplet Loss
(2018)

Triplet loss, popular for metric learning, has made a great success in many computer vision tasks, such as fine-grained image classification, image retrieval, and face recognition. Considering that the number of triplets grows cubically with the size of training data, triplet selection is thus indispensable for efficiently training with triplet loss. However, in practice, the training is usually very sensitive to the selection of triplets, e.g., it almost does not converge with randomly selected triplets and selecting the hardest triplets also leads to bad local minima. We argue that the bias in the selection of triplets degrades the performance of learning with triplet loss. In this paper, we propose a new variant of triplet loss, which tries to reduce the bias in triplet selection by adaptively correcting the distribution shift on the selected triplets. We refer to this new triplet loss as adapted triplet loss. We conduct a number of experiments on MNIST and Fashion-MNIST for image classification, and on CARS196, CUB200-2011, and Stanford Online Products for image retrieval. The experimental results demonstrate the effectiveness of the proposed method.
Min-BDeu and Max-BDeu Scores for Learning Bayesian Networks
(2014)

This work presents two new score functions based on the Bayesian Dirichlet equivalent uniform (BDeu) score for learning Bayesian network structures. They consider the sensitivity of BDeu to varying parameters of the Dirichlet prior. The scores take on the most adversary and the most beneficial priors among those within a contamination set around the symmetric one. We build these scores in such way that they are decomposable and can be computed efficiently. Because of that, they can be integrated into any state-of-the-art structure learning method that explores the space of directed acyclic graphs and allows decomposable scores. Empirical results suggest that our scores outperform the standard BDeu score in terms of the likelihood of unseen data and in terms of edge discovery with respect to the true network, at least when the training sample size is small. We discuss the relation between these new scores and the accuracy of inferred models. Moreover, our new criteria can be used to identify the amount of data after which learning is saturated, that is, additional data are of little help to improve the resulting model.
A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis
(2016)

This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach. HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.
Discriminative n-gram language modeling
(2007)

This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on maximizing the regularized conditional log-likelihood. The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. We describe a method based on regularized likelihood that makes use of the feature set given by the perceptron algorithm, and initialization with the perceptron’s weights; this method gives an additional 0.5% reduction in word error rate (WER) over training with the perceptron alone. The final system achieves a 1.8% absolute reduction in WER for a baseline first-pass recognition system (from 39.2% to 37.4%), and a 0.9% absolute reduction in WER for a multi-pass recognition system (from 28.9% to 28.0%). 2006 Elsevier Ltd. All rights reserved.
A Game-like Application for Dance Learning Using a Natural Human Computer Interface
(2015)

Game-based learning and gamification techniques are recently becoming a popular trend in the field of Technology Enhanced Learning. In this paper, we mainly focus on the use of game design elements for the transmission of Intangible Cultural Heritage (ICH) knowledge and, especially, for the learning of traditional dances. More specifically, we present a 3D game environment that employs an enjoyable natural human computer interface, which is based on the fusion of multiple depth sensors data in order to capture the body movements of the user/learner. In addition, the system automatically assesses the learner’s performance by utilizing a combination of Dynamic Time Warping (DTW) with Fuzzy Inference System (FIS) approach and provides feedback in a form of a score as well as instructions from a virtual tutor in order to promote self-learning. As a pilot use case, a Greek traditional dance, namely Tsamiko, has been selected. Preliminary small-scaled experiments with students of the Department of Physical Education and Sports Science at Aristotle University of Thessaloniki have shown the great potential of the proposed application.
Efficient tracing of failed nodes in sensor networks
(2002)

In sensor networks, nodes commonly rely on each other to route messages to a base station. Although this practice conserves power it can obscure the cause of a measurement outage in a portion of the network. For example, when a base station ceases to receive measurements from a region of nodes it can't immediately determine whether this is because of the destruction of all the nodes in that region (due to an enemy attack, for example) or merely the result of the failure of a few nodes bearing much of the routing load. Previous solutions to this problem typically consist of re-running the route-discovery protocol, a process that can be quite expensive in terms of the number of messages that must be exchanged. We demonstrate that the topology of the network can be efficiently conveyed to the base station allowing for the quick tracing of the identities of the failed nodes with moderate communication overhead. Our algorithms work in conjunction with the existing functions of the network, requiring the nodes to send no additional messages.
The role of anterior insular cortex in social emotions
(2010)

Functional neuroimaging investigations in the fields of social neuroscience and neuroeconomics indicate that the anterior insular cortex (AI) is consistently involved in empathy, compassion, and interpersonal phenomena such as fairness and cooperation. These findings suggest that AI plays an important role in social emotions, hereby defined as affective states that arise when we interact with other people and that depend on the social context. After we link the role of AI in social emotions to interoceptive awareness and the representation of current global emotional states, we will present a model suggesting that AI is not only involved in representing current states, but also in predicting emotional states relevant to the self and others. This model also proposes that AI enables us to learn about emotional states as well as about the uncertainty attached to events, and implies that AI plays a dominant role in decision making in complex and uncertain environments. Our review further highlights that dorsal and ventro-central, as well as anterior and posterior subdivisions of AI potentially subserve different functions and guide different aspects of behavioral regulation. We conclude with a section summarizing different routes to understanding other people’s actions, feelings and thoughts, emphasizing the notion that the predominant role of AI involves understanding others’ feeling and bodily states rather than their action intentions or abstract beliefs.
UMH: A Hardware-Based Unified Memory Hierarchy for Systems with Multiple Discrete GPUs
(2016)

In this article, we describe how to ease memory management between a Central Processing Unit (CPU) and one or multiple discrete Graphic Processing Units (GPUs) by architecting a novel hardware-based Unified Memory Hierarchy (UMH). Adopting UMH, a GPU accesses the CPU memory only if it does not find its required data in the directories associated with its high-bandwidth memory, or the NMOESI coherency protocol limits the access to that data. Using UMH with NMOESI improves performance of a CPU-multiGPU system by at least 1.92 × in comparison to alternative software-based approaches. It also allows the CPU to access GPUs modified data by at least 13 × faster.
Nonexistence of Certain Symmetrical Spherical Codes
(1993)

A spherical 1-code Wis any finite subset of the unit sphere in n dimensions S n-1 for which d(u, v) > I for every u, v from W, u ~ v. A spherical 1-code is symmetric ifu ~ Wimplies u fi W. The best upper bounds on the size of symmetric spherical codes on S n-~ were obtained in [1]. Here we obtain the same bounds by a similar method and improve these bounds for n = 5, 10, 14 and 22.